New method for online quality control of dwell position and dwell time in brachytherapy by using high-speed camera and neural networks.

Objective.To develop an online quality control (QC) system for accurate assessment of dwell position and dwell time in high-dose-rate (HDR) brachytherapy, and to investigate the potential of neural networks so as to improve the robustness and stability of the proposed system.Approach.An integrated framework was constructed using a Basler high-speed camera (144 fps, 1920 × 1200 pixels), custom illumination, and dedicated software. Experiments were conducted with a GammaMedPlus iX afterloader equipped with a stepping192Irsource, testing various step sizes (0.2 cm, 0.5 cm, 1.0 cm) and dwell times (2.0 s, 3.0 s, 10.0 s). The core online algorithm employed a frame-difference method for source tracking, while offline analysis evaluated the RT-DETRv2 neural network for source localization.Main results.The system achieved high spatial resolution (0.083 mm) and temporal resolution (7.0 ms). Primarily due to guidewire bending, positional deviations remained below 0.1 cm and increased with guidewire length. After position correction, the positional deviation was reduced to about 0.01 cm. Dwell time deviations were within 10.0 ms. RT-DETRv2 demonstrated outstanding localization accuracy (91% of predictions within 0.26 mm) in various conditions. However, its processing latency of 0.35 s per image makes it unsuitable for online monitoring but well-suited for offline or auxiliary verification in this system.Significance.This work presented a technically feasible online QC method for HDR brachytherapy that enabled precise verification of source delivery parameters. Moreover, the successful application of deep learning for source detection established a foundation for future enhancements in reliability and automation of the proposed QC system.